Conformal Prediction Under Covariate Shift
Conformal Prediction Under Covariate Shift
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发表时间：
2019-04
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通讯作者：
R. Tibshirani;R. Barber;E. Candès;Aaditya Ramdas
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作者：
R. Tibshirani;R. Barber;E. Candès;Aaditya Ramdas
我们扩展了保角预测方法，超越了可交换数据的情况。特别是，我们证明了对于测试和训练协变量分布不同的问题，可以使用共形预测的加权版本来计算无分布预测区间，但是这两个分布之间的似然比是已知的-或者，在实践中，可以通过访问大量未标记的数据(测试协变点)来准确地估计。我们的共形预测的加权扩展也更普遍地适用于数据满足某种加权可交换性概念的设置。我们讨论了我们新的共形方法的其他潜在应用，包括潜在变量和丢失数据的问题。
We extend conformal prediction methodology beyond the case of exchangeable data. In particular, we show that a weighted version of conformal prediction can be used to compute distribution-free prediction intervals for problems in which the test and training covariate distributions differ, but the likelihood ratio between these two distributions is known---or, in practice, can be estimated accurately with access to a large set of unlabeled data (test covariate points). Our weighted extension of conformal prediction also applies more generally, to settings in which the data satisfies a certain weighted notion of exchangeability. We discuss other potential applications of our new conformal methodology, including latent variable and missing data problems.